Determining semantic similarity between texts is important in many tasks in information retrieval such as search, query suggestion, automatic summarization and image finding. Many approaches have been suggested, based on lexical matching, handcrafted patterns, syntactic parse trees, external sources of structured semantic knowledge and distributional semantics. However, lexical features, like string matching, do not capture semantic similarity beyond a trivial level. Furthermore, handcrafted patterns and external sources of structured semantic knowledge cannot be assumed to be available in all circumstances and for all domains. Lastly, approaches depending on parse trees are restricted to syntactically well-formed texts, typically of one sentence in length. We investigate whether determining short text similarity is possible using only semantic features-where by semantic we mean, pertaining to a representation of meaning-rather than relying on similarity in lexical or syntactic representations. We use word embeddings, vector representations of terms, computed from unlabelled data, that represent terms in a semantic space in which proximity of vectors can be interpreted as semantic similarity. We propose to go from word-level to text-level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings. A novel feature of our approach is that an arbitrary number of word embedding sets can be incorporated. We derive multiple types of meta-features from the comparison of the word vectors for short text pairs, and from the vector means of their respective word embeddings. The features representing labelled short text pairs are used to train a supervised learning algorithm. We use the trained model at testing time to predict the semantic similarity of new, unlabelled pairs of short texts. We show on a publicly available evaluation set commonly used for the task of semantic similarity that our method outperforms baseline methods that work under the same conditions.

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of highquality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.

Word meanings change over time. Detecting shifts in meaning for particular words has been the focus of much research recently. We address the complementary problem of monitoring shifts in vocabulary over time. That is, given a small seed set of words, we are interested in monitoring which terms are used over time to refer to the underlying concept denoted by the seed words.In this paper, we propose an algorithm for monitoring shifts in vocabulary over time, given a small set of seed terms. We use distributional semantic methods to infer a series of semantic spaces over time from a large body of time-stamped unstructured textual documents. We construct semantic networks of terms based on their representation in the semantic spaces and use graph-based measures to calculate saliency of terms. Based on the graph-based measures we produce ranked lists of terms that represent the concept underlying the initial seed terms over time as final output.As the task of monitoring shifting vocabularies over time for an ad hoc set of seed words is, to the best of our knowledge, a new one, we construct our own evaluation set. Our main contributions are the introduction of the task of ad hoc monitoring of vocabulary shifts over time, the description of an algorithm for tracking shifting vocabularies over time given a small set of seed words, and a systematic evaluation of results over a substantial period of time (over four decades). Additionally, we make our newly constructed evaluation set publicly available.

Knowledge graphs, organizing structured information about entities, and their attributes and relationships, are ubiquitous today. Entities, in this context, are usually taken to be anyone or anything considered to be globally important. This, however, rules out many entities people interact with on a daily basis. In this position paper, we present the concept of personal knowledge graphs: resources of structured information about entities personally related to its user, including the ones that might not be globally important. We discuss key aspects that separate them for general knowledge graphs, identify the main challenges involved in constructing and using them, and define a r esearch agenda.

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Text-to-speech systems are typically evaluated on single sentences. When long-form content, such as data consisting of full paragraphs or dialogues is considered, evaluating sentences in isolation is not always appropriate as the context in which the sentences are synthesized is missing.In this paper, we investigate three different ways of evaluating the naturalness of long-form text-to-speech synthesis. We compare the results obtained from evaluating sentences in isolation, evaluating whole paragraphs of speech, and presenting a selection of speech or text as context and evaluating the subsequent speech. We find that, even though these three evaluations are based upon the same material, the outcomes differ per setting, and moreover that these outcomes do not necessarily correlate with each other. We show that our findings are consistent between a single speaker setting of read paragraphs and a two-speaker dialogue scenario. We conclude that to evaluate the quality of long-form speech, the traditional way of evaluating sentences in isolation does not suffice, and that multiple evaluations are required.

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to many di erent approaches for many di erent IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research. It covers key architectures, as well as the most promising future directions. *